Update README.md

Author: tirthajyoti

README.md

@@ -20,11 +20,11 @@ Jupyter notebooks covering a wide range of functions and operations on the topic
 * Simple linear regression with t-statistic generation
 <img src="https://slideplayer.com/slide/6053182/20/images/10/Simple+Linear+Regression+Model.jpg" width="400" height="300"/>
 
-* Multiple ways to do linear regression in Python and their speed comparison ([check the article I wrote on freeCodeCamp](https://medium.freecodecamp.org/data-science-with-python-8-ways-to-do-linear-regression-and-measure-their-speed-b5577d75f8b))
+* Multiple ways to perform linear regression in Python and their speed comparison ([check the article I wrote on freeCodeCamp](https://medium.freecodecamp.org/data-science-with-python-8-ways-to-do-linear-regression-and-measure-their-speed-b5577d75f8b))
 * Multi-variate regression with regularization
 <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/f/f8/L1_and_L2_balls.svg/300px-L1_and_L2_balls.svg.png"/>
 
-* Polynomial regression with how to use ***scikit-learn pipeline feature*** ([check the article I wrote on *Towards Data Science*](https://towardsdatascience.com/machine-learning-with-python-easy-and-robust-method-to-fit-nonlinear-data-19e8a1ddbd49))
+* Polynomial regression using ***scikit-learn pipeline feature*** ([check the article I wrote on *Towards Data Science*](https://towardsdatascience.com/machine-learning-with-python-easy-and-robust-method-to-fit-nonlinear-data-19e8a1ddbd49))
 * Decision trees and Random Forest regression (showing how the Random Forest works as a robust/regularized meta-estimator rejecting overfitting)
 
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